Ford GoBike System Data

by (Amr saeed)

This data set of information about individual rides made in a bike-sharing system from the greater San Francisco Bay area. this dataset consist of 183412 trip,db image taken @2019

Investigation Overview

In analytics, what I want is to show the analytical differences between the different elements in the available data How does the trip time affect the location of its start and the gender of the rider

Dataset Overview

the structure of data has 16 features and 183412 trip those are (duration_sec, start_time, end_time, start_station_id, start_station_name, start_station_latitude, start_station_longitude, end_station_id, end_station_name, end_station_latitude ,end_station_longitude, bike_id, user_type, member_birth_year, member_gender, bike_share_for_all_trip).

I had

1- drop null vlaues

2- scaling duration in seconds to duration in minutes

3- dropping [other] type of gender for simplecity

4- scaling [member_birth_year] feature to [member_age]

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Trip Duration in Minute

as we see the first plot is not clear enough to fitch the most tripes duration distribution so I took the duration only to the range of 100 minute to provide better clearance

conclusion

the most trips are between 5 to 15 minutes (as the normal distribution centered) and shown above

plotting top 60 start trip distinations

the top distnation is ("market St") & ("san francisco Station 2")

I conclude that those two stations have the highest population so we need to provide more bikes

plotting the start hour of the trip

The trip distribution over day hours peaks around two timeframes, 7am-9am and 4pm-6pm, during typical rush hours.

trip duration according to each gender

stands on the univariate resultes active users grouped at 20:45 years-old

concluded that:

> 1- the week days are the working and collage days

> 2- the day hours are working start & end hours of work

> 3- that workers and collage students are the top clients

Text(0.5, 1.0, 'the trip duration according to agent gender')

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conclusion

> from previous plot we get that the male users are usimg more rides while comparing with female and other with long durations , other types of customers are taking long rides while they are older (50 - 60)

>The multivariate exploration confirmed the previous explorations and figures.

>The rides are mainly concentrated on rush hours Monday through Friday,

indicates that workers and collage students are the top clients,

>The longest rides are in weekends due to jamming.

the number of users for male is higher but percentage is higher for women in trip duration.

> the week days are the working and collage days

> the day hours are working start & end hours of work

> that workers and collage students are the top clients

> the top crowded station at rush hours varing from the the crowded station during the day

and

> I conclude that people avoid crowds at peak times unless the trip is a working or study trip